A new model for learning from multinomial data has recently been developed, giving predictive inferences in the form of lower and upper probabilities for a future observation. Apart from the past observations, no information on the sample space is assumed, so explicitly no assumptions are made on the number of possible categories. In this paper, we briefly present the general lower and upper pr...

1.1. Overview. In order to answer some questions in the theory of operator algebras Dykema and Haagerup started investigation of the, so–called, triangular operator T [DH01]. Currently there are many different descriptions of this operator: in terms of random matrices, in terms of free probability theory and a purely combinatorial one (and we will recall them in the following). Dykema and Haage...

A Bayesian Dirichlet-Multinomial Test for Cross-Group Differences by Yuhan Chen Department of Statistical Science Duke University

The asymptotic behaviour with respect to n of sums of type ∑

Ad M. M versit E-ma similar forgetting rates for both implicit and explicit instructions. The current study evaluated forgetting for implicit and explicit word-fragment completion. In Experiment 1, forgetting rates were compared for implicit and explicit task performance. Forgetting rates did not differ significantly between the two tasks. In Experiment 2, conscious and automatic memory estimat...

Multinomial Logistic Regression is a well-studied tool for classification and has been widely used in fields like image processing, computer vision and, bioinformatics, to name few. Under supervised scenario, model learns weight vector differentiate between any two classes by optimizing over the likelihood objective. With advent of big data, inundation data resulted large dimensional also given...